luojiyin1987

Create or update wiki concept files from knowledge base summaries. Use when the user wants to build or refresh concept articles, populate Wiki/Concepts, or run the concept-creation step after summarizing new files.

Expand thin concept articles in the knowledge wiki using web search. Targets concepts with minimal prose assembled from few sources and supplements them with authoritative external information. Run after accumulating new concepts or when articles feel sparse.

Audit and repair the knowledge wiki. Detects orphan summaries (source deleted), broken wikilinks, and orphan concept files. Run periodically after accumulating new content or reorganizing source files.

Interactively find and merge duplicate concept files in the knowledge wiki. Presents candidate pairs one at a time and asks whether to merge, dismiss, or skip each one. Run after accumulating new concepts or when the wiki feels redundant.

Generate or refresh wiki summaries for knowledge base markdown files. Use when the user wants to summarize notes, update the wiki, compile stale summaries, or process new knowledge base files into Wiki/Summaries.

Scan the wiki for cross-cutting connections, implicit relationships, contradictions, and gaps across concepts and summaries, then write synthesis concept files. Use when the user wants to discover non-obvious connections, synthesize the knowledge base, or run a reflection pass after adding new content.

Run typos CLI on files and produce LLM-reviewable spelling fixes with optional diff/apply.

Analyze a website, documentation site, API project, sitemap, GitHub README, or local docs directory and generate curated llms.txt plus optional llms-full.txt. Use when the user wants LLM-friendly docs output, page inclusion guidance, sitemap summaries, or content curation for AI consumption.

Analyze open-source license terms and compliance obligations for a repository. Use when asked to identify project licenses, explain what is required when reusing code, assess copyleft/commercial compatibility, or produce a practical risk checklist before shipping.

Use this skill when cleaning, refactoring, or reviewing Go code for symmetry, naming consistency, method ordering, receiver consistency, structure alignment, and local style preservation without changing behavior.

Diagnose Python performance problems with py-spy using safe profiling workflows, stack dumps, and flamegraph interpretation.

structured decision coaching for uncertain, high-consequence choices. use when the user asks for help making, evaluating, comparing, or stress-testing a decision in investing, career, business, product, strategy, hiring, purchasing, or personal planning. triggers include requests to use bayesian thinking, expected value, decision process, evidence weighting, risk assessment, pros and cons with rigor, pre-mortem, go/no-go recommendation, alpha from news, demand inflection, financial-statement translation, small-cap elasticity, market misclassification, or a repeatable decision framework. helps convert messy inputs into assumptions, evidence, probabilities, options, expected value, risks, action rules, and review criteria.